5 papers
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Fei Wang, Xingchen Wan, Ruoxi Sun +2
Retrieval augmented generation (RAG), while effectively integrating external knowledge to address the inherent limitations of large language models (LLMs), can be hindered by imper…
Offset Unlearning for Large Language Models
James Y. Huang, Wenxuan Zhou, Fei Wang +4
Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as c…
From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning
Nan Xu, Fei Wang, Sheng Zhang +2
Motivated by in-context learning (ICL) capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities w…
Monotonic Paraphrasing Improves Generalization of Language Model Prompting
Qin Liu, Fei Wang, Nan Xu +3
Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task. One commonly recognized factor for this phenomenon is the model's…
Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models
Tinghui Zhu, Qin Liu, Fei Wang +2
Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities for capturing and reasoning over multimodal inputs. However, these models are prone to parametric kno…